Hybrid Thermal Modeling With LPTN-Informed Neural Network for Multinode Temperature Estimation in PMSM
Zirui Liu, Wubin Kong, Xinggang Fan, Zimin Li, Kai Peng, Ronghai Qu
Abstract
To achieve improved multi-node temperature estimation with limited training data in Permanent Magnet Synchronous Motors (PMSMs), a novel approach of a Lumped-Parameter Thermal Network (LPTN)-informed neural network is proposed in this paper. Firstly, the parameter and model uncertainties of third or higher-order LPTNs with global parameter identification for temperature estimation are systematically stated based on numerical analysis. Then, a two-step parameter identification strategy for a third-order LPTN with simplified thermal transfer paths is proposed to resolve parameter uncertainty. This strategy uses only air-gap structure information to make all parameters converge to their unique solutions without the need for additional geometrical parameters or material features. In response to model uncertainty, an LPTNinformed Long Short-Term Memory (LSTM) framework is designed to compensate for model unaccounted errors and extend temperature estimation nodes that the highly abstract low-order LPTN does not consider. Experimental temperature estimation results validate the effectiveness of the proposed LPTN-informed LSTM framework under a limited 23.8 hours of training data.